Study Questions:

What are the baseline predictors of long-term bleeding risk for patients with acute coronary syndrome (ACS) and treated with dual antiplatelet therapy (DAPT)?

Methods:

The investigators analyzed 9,240 patients with unstable angina/non–ST-segment elevation myocardial infarction (NSTEMI) from the TRILOGY ACS (Targeted Platelet Inhibition to Clarify the Optimal Strategy to Medically Manage Acute Coronary Syndromes) trial, who were managed without revascularization and treated with DAPT for a median of 14.8 months to develop a longitudinal bleeding risk prediction model. A variable selection method, in conjunction with Cox proportional hazards regression, was implemented to help reduce the number of predictors to create a more parsimonious tool for predicting long-term bleeding risks.

Results:

The investigators identified 10 significant baseline predictors of noncoronary artery bypass grafting-related GUSTO (Global Use of Strategies to Open Occluded Arteries) severe/life-threatening/moderate bleeding: age, sex, weight, NSTEMI (vs. unstable angina), angiography performed before randomization, prior peptic ulcer disease, creatinine, systolic blood pressure, hemoglobin, and treatment with beta-blocker. The five significant baseline predictors of TIMI (Thrombolysis In Myocardial Infarction) major or minor bleeding included age, sex, angiography performed before randomization, creatinine, and hemoglobin. The models showed good predictive accuracy with Therneau’s C-indices: 0.78 (SE = 0.024) for the GUSTO model and 0.67 (SE = 0.023) for the TIMI model. Internal validation with bootstrapping gave similar C-indices of 0.77 and 0.65, respectively. External validation demonstrated an attenuated C-index for the GUSTO model (0.69), but not the TIMI model (0.68).

Conclusions:

The authors concluded that longitudinal bleeding risks during treatment with DAPT in patients with ACS can be reliably predicted using selected baseline characteristics.

Perspective:

This study reports that individualized, longitudinal risk of bleeding during DAPT treatment after hospitalization for ACS can be reliably predicted with simple bleeding risk prediction models. However, it should be noted that these models were derived from a population of medically managed patients with unstable angina/NSTEMI who primarily experienced spontaneous and not procedural-related bleeding events during DAPT following the index ACS event, and the results may not be considered applicable to the broader ACS population. Additional evaluation of the TRILOGY ACS bleeding models in other contemporary ACS databases and in invasively managed patients is indicated to validate this model.